pkg_search() starts a new search query, or shows the details of the
previous query, if called without arguments.
ps() is an alias to pkg_search().
more() retrieves that next page of results for the previous query.
Search query string. If this argument is missing or
NULL, then the results of the last query are printed, in
short and long formats, in turns for successive
pkg_search() calls. If this argument is missing, then all
other arguments are ignored.
Default formatting of the results. short only outputs the name and title of the packages, long also prints the author, last version, full description and URLs. Note that this only affects the default printing, and you can still inspect the full results, even if you specify short here.
Where to start listing the results, for pagination.
The number of results to list.
Object to summarize.
Additional arguments, ignored currently.
Object to print.
A data frame with columns:
score: Score of the hit. See Section Scoring for some details.
package: Package name.
version: Latest package version.
title: Package title.
description: Short package description.
date: Time stamp of the last release.
maintainer_name: Name of the package maintainer.
maintainer_email: Email address of the package maintainer.
revdeps: Number of (strong and weak) reverse dependencies of the
package.
downloads_last_month: Raw number of package downloads last month,
from the RStudio CRAN mirror.
license: Package license.
url: Package URL(s).
bugreports: URL of issue tracker, or email address for bug reports.
Note that the search needs a working Internet connection.
# Example
ps("survival")
#> - "survival" --------------------------------- 1131 packages in 0.012 seconds -
#> # package version by @ title
#> 1 100 survival 3.5.8 Terry M Therneau 2M Survival Analysis
#> 2 6 KMsurv 0.1.5 Jun Yan 11y Data sets from...
#> 3 6 rpart 4.1.23 Beth Atkinson 5M Recursive Part...
#> 4 5 randomForestSRC 3.2.3 Udaya B. Kogalur 5M Fast Unified R...
#> 5 5 timereg 2.0.5 Thomas Scheike 1y Flexible Regre...
#> 6 4 pec 2023.4.12 Thomas A. Gerds 1y Prediction Err...
#> 7 4 survC1 1.0.3 Hajime Uno 3y C-Statistics f...
#> 8 4 muhaz 1.2.6.4 David Winsemius 3y Hazard Functio...
#> 9 3 survivalROC 1.0.3.1 Paramita Saha-Chaudhuri 1y Time-Dependent...
#> 10 3 multcomp 1.4.25 Torsten Hothorn 10M Simultaneous I...
# Pagination
ps("networks")
#> - "networks" ---------------------------------- 959 packages in 0.014 seconds -
#> # package version by @ title
#> 1 100 igraph 2.0.3 Kirill Müller 1M Network Analysis and...
#> 2 71 RCurl 1.98.1.14 CRAN Team 3M General Network (HTT...
#> 3 70 network 1.18.2 Carter T. Butts 5M Classes for Relation...
#> 4 52 nnet 7.3.19 Brian Ripley 1y Feed-Forward Neural ...
#> 5 36 sna 2.7.2 Carter T. Butts 5M Tools for Social Net...
#> 6 28 snow 0.4.4 Luke Tierney 2y Simple Network of Wo...
#> 7 24 ergm 4.6.0 Pavel N. Krivitsky 4M Fit, Simulate and Di...
#> 8 19 networkDynamic 0.11.4 Skye Bender-deMoll 4M Dynamic Extensions f...
#> 9 17 diagram 1.6.5 Karline Soetaert 4y Functions for Visual...
#> 10 16 RSNNS 0.4.17 Christoph Bergmeir 5M Neural Networks usin...
more()
#> - "networks" ---------------------------------- 959 packages in 0.013 seconds -
#> # package version by @ title
#> 11 14 neuralnet 1.44.2 Marvin N. Wright 5y Training of Neural Networks
#> 12 12 latentnet 2.11.0 Pavel N. Krivitsky 2M Latent Position and Clust...
#> 13 11 snowFT 1.6.1 Hana Sevcikova 7M Fault Tolerant Simple Net...
#> 14 11 gRain 1.4.1 Søren Højsgaard 5M Graphical Independence Ne...
#> 15 10 GeneNet 1.2.16 Korbinian Strimmer 2y Modeling and Inferring Ge...
#> 16 10 ndtv 0.13.3 Skye Bender-deMoll 1y Network Dynamic Temporal ...
#> 17 10 WGCNA 1.72.5 Peter Langfelder 5M Weighted Correlation Netw...
#> 18 10 qrnn 2.1.1 Alex J. Cannon 2M Quantile Regression Neura...
#> 19 9 GISSB 1.1 Sindre Mikael Haugen 1y Network Analysis on the N...
#> 20 9 igraphdata 1.0.1 Gabor Csardi 9y A Collection of Network D...
# Details
ps("visualization")
#> - "visualization" ---------------------------- 1733 packages in 0.014 seconds -
#> # package version by @ title
#> 1 100 ggplot2 3.5.0 Thomas Lin Pedersen 2M Create Elegant Data Visuali...
#> 2 87 igraph 2.0.3 Kirill Müller 1M Network Analysis and Visual...
#> 3 81 rgl 1.3.1 Duncan Murdoch 2M 3D Visualization Using OpenGL
#> 4 55 scales 1.3.0 Thomas Lin Pedersen 5M Scale Functions for Visuali...
#> 5 42 vcd 1.4.12 David Meyer 4M Visualizing Categorical Data
#> 6 35 ROCR 1.0.11 Felix G.M. Ernst 4y Visualizing the Performance...
#> 7 24 klaR 1.7.3 Uwe Ligges 4M Classification and Visualiz...
#> 8 20 ggmap 4.0.0 David Kahle 5M Spatial Visualization with ...
#> 9 19 lattice 0.22.6 Deepayan Sarkar 1M Trellis Graphics for R
#> 10 18 corrplot 0.92 Taiyun Wei 2y Visualization of a Correlat...
ps()
#> - "visualization" ---------------------------- 1733 packages in 0.014 seconds -
#>
#> 1 ggplot2 @ 3.5.0 Thomas Lin Pedersen, 2 months ago
#> -----------------
#> # Create Elegant Data Visualisations Using the Grammar of Graphics
#> A system for 'declaratively' creating graphics, based on "The Grammar
#> of Graphics". You provide the data, tell 'ggplot2' how to map
#> variables to aesthetics, what graphical primitives to use, and it
#> takes care of the details.
#> https://ggplot2.tidyverse.org
#> https://github.com/tidyverse/ggplot2
#>
#> 2 igraph @ 2.0.3 Kirill Müller, about a month ago
#> ----------------
#> # Network Analysis and Visualization
#> Routines for simple graphs and network analysis. It can handle large
#> graphs very well and provides functions for generating random and
#> regular graphs, graph visualization, centrality methods and much
#> more.
#> https://r.igraph.org/
#> https://igraph.org/
#> https://igraph.discourse.group/
#>
#> 3 rgl @ 1.3.1 Duncan Murdoch, 2 months ago
#> -------------
#> # 3D Visualization Using OpenGL
#> Provides medium to high level functions for 3D interactive graphics,
#> including functions modelled on base graphics (plot3d(), etc.) as
#> well as functions for constructing representations of geometric
#> objects (cube3d(), etc.). Output may be on screen using OpenGL, or
#> to various standard 3D file formats including WebGL, PLY, OBJ, STL as
#> well as 2D image formats, including PNG, Postscript, SVG, PGF.
#> https://github.com/dmurdoch/rgl
#> https://dmurdoch.github.io/rgl/
#>
#> 4 scales @ 1.3.0 Thomas Lin Pedersen, 5 months ago
#> ----------------
#> # Scale Functions for Visualization
#> Graphical scales map data to aesthetics, and provide methods for
#> automatically determining breaks and labels for axes and legends.
#> https://scales.r-lib.org
#> https://github.com/r-lib/scales
#>
#> 5 vcd @ 1.4.12 David Meyer, 4 months ago
#> --------------
#> # Visualizing Categorical Data
#> Visualization techniques, data sets, summary and inference procedures
#> aimed particularly at categorical data. Special emphasis is given to
#> highly extensible grid graphics. The package was package was
#> originally inspired by the book "Visualizing Categorical Data" by
#> Michael Friendly and is now the main support package for a new book,
#> "Discrete Data Analysis with R" by Michael Friendly and David Meyer
#> (2015).
#>
#> 6 ROCR @ 1.0.11 Felix G.M. Ernst, 4 years ago
#> ---------------
#> # Visualizing the Performance of Scoring Classifiers
#> ROC graphs, sensitivity/specificity curves, lift charts, and
#> precision/recall plots are popular examples of trade-off
#> visualizations for specific pairs of performance measures. ROCR is a
#> flexible tool for creating cutoff-parameterized 2D performance curves
#> by freely combining two from over 25 performance measures (new
#> performance measures can be added using a standard interface). Curves
#> from different cross-validation or bootstrapping runs can be averaged
#> by different methods, and standard deviations, standard errors or box
#> plots can be used to visualize the variability across the runs. The
#> parameterization can be visualized by printing cutoff values at the
#> corresponding curve positions, or by coloring the curve according to
#> cutoff. All components of a performance plot can be quickly adjusted
#> using a flexible parameter dispatching mechanism. Despite its
#> flexibility, ROCR is easy to use, with only three commands and
#> reasonable default values for all optional parameters.
#> http://ipa-tys.github.io/ROCR/
#>
#> 7 klaR @ 1.7.3 Uwe Ligges, 4 months ago
#> --------------
#> # Classification and Visualization
#> Miscellaneous functions for classification and visualization, e.g.
#> regularized discriminant analysis, sknn() kernel-density naive Bayes,
#> an interface to 'svmlight' and stepclass() wrapper variable selection
#> for supervised classification, partimat() visualization of
#> classification rules and shardsplot() of cluster results as well as
#> kmodes() clustering for categorical data, corclust() variable
#> clustering, variable extraction from different variable clustering
#> models and weight of evidence preprocessing.
#> https://statistik.tu-dortmund.de
#>
#> 8 ggmap @ 4.0.0 David Kahle, 5 months ago
#> ---------------
#> # Spatial Visualization with ggplot2
#> A collection of functions to visualize spatial data and models on top
#> of static maps from various online sources (e.g Google Maps and
#> Stamen Maps). It includes tools common to those tasks, including
#> functions for geolocation and routing.
#> https://github.com/dkahle/ggmap
#>
#> 9 lattice @ 0.22.6 Deepayan Sarkar, about a month ago
#> ------------------
#> # Trellis Graphics for R
#> A powerful and elegant high-level data visualization system inspired
#> by Trellis graphics, with an emphasis on multivariate data. Lattice
#> is sufficient for typical graphics needs, and is also flexible enough
#> to handle most nonstandard requirements. See ?Lattice for an
#> introduction.
#> https://lattice.r-forge.r-project.org/
#>
#> 10 corrplot @ 0.92 Taiyun Wei, 2 years ago
#> ------------------
#> # Visualization of a Correlation Matrix
#> Provides a visual exploratory tool on correlation matrix that
#> supports automatic variable reordering to help detect hidden patterns
#> among variables.
#> https://github.com/taiyun/corrplot
# See the underlying data frame
ps("ropensci")
#> - "ropensci" ----------------------------------- 267 packages in 0.04 seconds -
#> # package version by @ title
#> 1 100 fastMatMR 1.2.5 Rohit Goswami 6M High-Performance ...
#> 2 98 rfisheries 0.2 Karthik Ram 8y 'Programmatic Int...
#> 3 96 mcbette 1.15.2 Richèl J.C. Bilderbeek 7M Model Comparison ...
#> 4 83 chromer 0.8 Karl W Broman 26d Interface to Chro...
#> 5 73 workloopR 1.1.4 Vikram B. Baliga 3y Analysis of Work ...
#> 6 68 opentripplanner 0.5.1 Malcolm Morgan 1y Setup and connect...
#> 7 56 googleLanguageR 0.3.0 Mark Edmondson 4y Call Google's 'Na...
#> 8 55 rtika 2.7.0 Sasha Goodman 1y R Interface to 'A...
#> 9 54 yfR 1.1.0 Marcelo Perlin 1y Downloads and Org...
#> 10 53 hydroscoper 1.4.1 Konstantinos Vantas 3y Interface to the ...
ps()[]
#> # A data frame: 10 × 14
#> score package version title description date maintainer_name
#> <dbl> <chr> <pckg_> <chr> <chr> <dttm> <chr>
#> 1 394. fastMatMR 1.2.5 "Hig… "An interf… 2023-11-03 21:00:06 Rohit Goswami
#> 2 386. rfisheri… 0.2 "'Pr… "A program… 2016-02-19 08:50:03 Karthik Ram
#> 3 379. mcbette 1.15.2 "Mod… "'BEAST2' … 2023-09-27 08:00:02 Richèl J.C. Bi…
#> 4 329. chromer 0.8 "Int… "A program… 2024-03-27 03:40:02 Karl W Broman
#> 5 289. workloopR 1.1.4 "Ana… "Functions… 2021-05-06 07:10:02 Vikram B. Bali…
#> 6 269. opentrip… 0.5.1 "Set… "Setup and… 2023-02-02 16:30:02 Malcolm Morgan
#> 7 219. googleLa… 0.3.0 "Cal… "Call 'Goo… 2020-04-19 12:40:02 Mark Edmondson
#> 8 216. rtika 2.7.0 "R I… "Extract t… 2023-05-04 22:10:02 Sasha Goodman
#> 9 215. yfR 1.1.0 "Dow… "Facilitat… 2023-02-16 11:20:02 Marcelo Perlin
#> 10 211. hydrosco… 1.4.1 "Int… "R interfa… 2021-05-14 15:30:03 Konstantinos V…
#> # ℹ 7 more variables: maintainer_email <chr>, revdeps <int>,
#> # downloads_last_month <int>, license <chr>, url <chr>, bugreports <chr>,
#> # package_data <I<list>>